We propose a robust spectrum sensing framework based on deep learning. The received signals at the secondary user's receiver are filtered, sampled and then directly fed into a convolutional neural network. Although this deep sensing is effective when operating in the same scenario as the collected training data, the sensing performance is degraded when it is applied in a different scenario with different wireless signals and propagation. We incorporate transfer learning into the framework to improve the robustness. Results validate the effectiveness as well as the robustness of the proposed deep spectrum sensing framework.
Optimal MAC based cooperative spectrum sensing in cognitive radio networks SCIENCE CHINA Information Sciences 55, 1388 (2012); Cooperative spectrum sensing based on stochastic resonance in cognitive radio networks SCIENCE CHINA Information Sciences 57, 022306 (2014); Robust cooperative spectrum sensing schemes for fading channels in cognitive radio networks
This paper addresses the design of the power-limited intelligent adversary for sensing deception in a cognitive radio network. The average number of successfully spoofed bands by the adversary is analyzed, which can be expressed in terms of the individual spoofing probability on each band. The worst-case sensing deception strategy is obtained by maximizing the average number of successfully spoofed bands, under the adversary's power constraint. Specifically, for a cognitive radio network where energy detection is utilized by secondary users, it is shown that the worst-case deception strategy is equal-power, partialband spoofing.
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